Setup

library(dplyr)
library(tidyr)

bigfoot <- readr::read_csv(here::here("data/bigfoot.csv"))

Motivation

So far we have taken the data set at face value; but if we explore we may find some issues. For example: what is “Unknown” for the season?

bigfoot |> count(season)

# A tibble: 5 × 2
  season      n
  <chr>   <int>
1 Fall     1492
2 Spring    825
3 Summer   1867
4 Unknown    92
5 Winter    745

Excourse: vectorized control flow

Generally, most languages - including R - have some form of “control flow” logic:

if (condition) {
  do_thing()
} else {
  do_other_thing()
}

Excourse: vectorized control flow

if (bigfoot$season == "Unknown") print("This one!")

Error in if (bigfoot$season == "Unknown") print("This one!"): Bedingung hat Länge > 1

Excourse: vectorized control flow

dplyr provides an if_else() that is vectorized (no need to write loops or similar), which would look like:

if (condition) {
  do_thing()
} else {
  do_other_thing()
}

if_else(condition, 
  do_thing(), 
  do_other_thing()
)

• if_else() automatically handles missing values, while base R’s ifelse() does not! You can also determine how they should be handled by setting the missing-argument.

Excourse: vectorized control flow

If condition is TRUE, it will execute true, else it executes false:

bigfoot |> mutate(reliable = if_else(classification == "Class A", 1, 0))

…will create a “dummy”-vector that is 1 if classification == "Class A" and 0 otherwise (for “unreliable” sightings).

Excourse: vectorized control flow

But control flow may become more complex:

if (condition) {
  do_thing()
} else if (other_condition) {
  do_other_thing()
} else {
  do_something_else()
}

Excourse: vectorized control flow

The solution is case_when():

if (condition) {
  do_thing()
} else if (other_condition) {
  do_other_thing()
} else {
  do_something_else()
}

case_when(
  condition ~ do_thing(),
  other_condition ~ do_other_thing(),
  .default = do_something_else()
)

Nonsensical records & missing values: imputation

Back to our problem:

bigfoot |> 
  mutate(
    # First add the month:
    month = lubridate::month(date),
    # Then impute:
    season = case_when(
      season != "Unknown" ~ season, # if season not unkown just keep it
      month %in% c(3:5) ~ "Spring", # otherwise step through these...
      month %in% c(6:9) ~ "Summer",
      month %in% c(10:11) ~ "Fall",
      month %in% c(12, 1, 2) ~ "Winter",
      .default = NA                 # ...and to this if no condition matches.
    )
  )